Growing up in China, I initially wanted to be an English major and become an English translator.
If I were to write a list under the heading “Things I Wish I Mastered in School,” Excel would be surprisingly close to the top. One of the biggest motivations that drove me to pursue a career as an actuary, was that it was consistently ranked as one of the top jobs in America. Raise Your Odds® is registered in the US Patent and Trademark Office and Earned Level™ is a trademark of Coaching Actuaries. We can all agree that a big part of career success is networking. If you haven’t started, get started! What one piece of advice would you give to someone like me who wants to enter the industry? Keep them informed about your status, especially if someone they connected you with helps you. Disclaimer: The content of this blog post is the sole opinion of the author and is not associated with Coaching Actuaries, its team members or its brand.
Written by Elizabeth Wortman: With career fairs and interviews just around the corner, most students are contemplating various companies of interest as well as what they need to do to land a position. Here is a recent example of something that started out as a typo, then became a mathematical erratum, and ended up as just a typo.
Determine the limit of a as the sample mean goes to infinity, where a is the maximum likelihood estimator of ?. Written by Paul Ireland, VP of Product Management: As you may have heard, we launched a new format for the ASM Manual at the beginning of February.

When it comes to careers, most of us start with a dream, but we realize soon enough that real career is not just about what we want to be but also about what we can do. Coaching Actuaries provides an opportunity for actuaries to share their ideas, but does not claim any responsibility for the content provided. I first heard about the club when it came up in a google search for a social networking site for actuaries.
An important, but underrated topic related to landing an internship and pursuing success, is networking. In the first two printings of the Exam C manual 16th edition, a question from CAS 4B Spring 1998 was listed as exercise 33.36. Based on your feedback, we wanted to bring you a more effective and enjoyable studying experience. One item we had changed up was our eCourse video format, so we were definitely anxious to see what you all thought of the improvements. It was there I met Richard London, a former professor at University of Connecticut and founder of Actex.
Career transitions may be unexpected and often difficult, but a happy outcome is usually possible. After passing the FM exam, I was admitted into the club by my academic adviser from college.
But I wasn’t prepared for how difficult it would be live in another country and start a new life without family and friends around.
For example, I used to refer in one of my manuals to an unbiased sample standard deviation, until a student pointed out to me that no such thing exists.

The first few weeks were difficult because of the language barrier, the new culture, and the loneliness. Upon learning that passing actuarial exams was the only way to become one, I studied and took exams. Family friends, former colleagues, managers, fellow members of social and professional groups.
One can calculate an unbiased sample variance by dividing the sum of square deviations from sample mean by n ? 1 rather than by n, but once one applies any nonlinear operation to an unbiased estimator, it is no longer unbiased.
But then at least one xi is greater than or equal to the sample mean, and ?ln(1 + xi) is then greater than or equal to ln(1 + x). So just because the sample mean goes to infinity, why must this denominator go to infinity? As x goes to infinity, ln(1 + x) will go to infinity and the maximum likelihood estimator must therefore go to 0.
It seems like it need not go to infinity, since the geometric mean is less than the (arithmetic) sample mean! After graduating with a Bachelor’s degree in Computer Science, I expected to find a job in the IT industry before long. Namely, suppose the sample consists of n?1 very tiny numbers, say, and one number equal to .

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